title: Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer.
abstract: Nomogram based on multiple logistic regression (MLR) is a standard technique for predicting diagnostic and treatment outcomes in medical fields. However, the applicability of MLR to data mining of clinical information is limited. To overcome these issues, we have developed prediction models using ensembles of alternative decision trees (ADTree). Here, we compare the performance of MLR and ADTree models in terms of robustness against missing values. As a case study, we employ datasets including pathological complete response (pCR) of neoadjuvant therapy, one of the most important decision-making factors in the diagnosis and treatment of primary breast cancer. Ensembled ADTree models are more robust against missing values than MLR. Sufficient robustness is attained at low boosting and ensemble number, and is compromised as these numbers increase.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sugimoto
authors.forename: Masahiro
authors.initials: M
authors.lastname: Takada
authors.forename: Masakazu
authors.initials: M
authors.lastname: Toi
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000188:drug therapy / Q000473:pathology; D057225:Data Mining; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D020360:Neoadjuvant Therapy / Q000379:methods; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D016896:Treatment Outcome